Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 1 | // |
Colm Donelan | 7bcae3c | 2024-01-22 10:07:14 +0000 | [diff] [blame] | 2 | // Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include "TestUtils.hpp" |
| 9 | |
| 10 | #include <armnn_delegate.hpp> |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 11 | #include <DelegateTestInterpreter.hpp> |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 12 | |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 13 | #include <tensorflow/lite/version.h> |
| 14 | |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 15 | namespace |
| 16 | { |
| 17 | |
| 18 | std::vector<char> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode, |
| 19 | tflite::TensorType tensorType, |
| 20 | std::vector<int32_t>& inputTensorShape, |
| 21 | const std::vector <int32_t>& outputTensorShape, |
| 22 | const int32_t inputTensorNum, |
| 23 | int32_t axis = 0, |
| 24 | float quantScale = 1.0f, |
| 25 | int quantOffset = 0) |
| 26 | { |
| 27 | using namespace tflite; |
| 28 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 29 | |
| 30 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 31 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 32 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 33 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 34 | |
| 35 | auto quantizationParameters = |
| 36 | CreateQuantizationParameters(flatBufferBuilder, |
| 37 | 0, |
| 38 | 0, |
| 39 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 40 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 41 | |
| 42 | std::vector<int32_t> operatorInputs{}; |
| 43 | const std::vector<int32_t> operatorOutputs{inputTensorNum}; |
| 44 | std::vector<int> subgraphInputs{}; |
| 45 | const std::vector<int> subgraphOutputs{inputTensorNum}; |
| 46 | |
| 47 | std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1); |
| 48 | for (int i = 0; i < inputTensorNum; ++i) |
| 49 | { |
| 50 | tensors[i] = CreateTensor(flatBufferBuilder, |
| 51 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 52 | inputTensorShape.size()), |
| 53 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 54 | 1, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 55 | flatBufferBuilder.CreateString("input" + std::to_string(i)), |
| 56 | quantizationParameters); |
| 57 | |
| 58 | // Add number of inputs to vector. |
| 59 | operatorInputs.push_back(i); |
| 60 | subgraphInputs.push_back(i); |
| 61 | } |
| 62 | |
| 63 | // Create output tensor |
| 64 | tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, |
| 65 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 66 | outputTensorShape.size()), |
| 67 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 68 | 2, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 69 | flatBufferBuilder.CreateString("output"), |
| 70 | quantizationParameters); |
| 71 | |
| 72 | // create operator |
| 73 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions; |
| 74 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union(); |
| 75 | |
| 76 | flatbuffers::Offset <Operator> controlOperator = |
| 77 | CreateOperator(flatBufferBuilder, |
| 78 | 0, |
| 79 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 80 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 81 | operatorBuiltinOptionsType, |
| 82 | operatorBuiltinOptions); |
| 83 | |
| 84 | flatbuffers::Offset <SubGraph> subgraph = |
| 85 | CreateSubGraph(flatBufferBuilder, |
| 86 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 87 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 88 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 89 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 90 | |
| 91 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 92 | flatBufferBuilder.CreateString("ArmnnDelegate: Concatenation Operator Model"); |
| 93 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); |
| 94 | |
| 95 | flatbuffers::Offset <Model> flatbufferModel = |
| 96 | CreateModel(flatBufferBuilder, |
| 97 | TFLITE_SCHEMA_VERSION, |
| 98 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 99 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 100 | modelDescription, |
| 101 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 102 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 103 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 104 | |
| 105 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 106 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 107 | } |
| 108 | |
| 109 | std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode, |
| 110 | tflite::TensorType tensorType, |
| 111 | std::vector<int32_t>& input0TensorShape, |
| 112 | std::vector<int32_t>& input1TensorShape, |
| 113 | const std::vector <int32_t>& outputTensorShape, |
| 114 | std::vector<int32_t>& axisData, |
| 115 | const bool keepDims, |
| 116 | float quantScale = 1.0f, |
| 117 | int quantOffset = 0) |
| 118 | { |
| 119 | using namespace tflite; |
| 120 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 121 | |
| 122 | std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 123 | buffers[0] = CreateBuffer(flatBufferBuilder); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 124 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 125 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 126 | sizeof(int32_t) * axisData.size())); |
| 127 | |
| 128 | auto quantizationParameters = |
| 129 | CreateQuantizationParameters(flatBufferBuilder, |
| 130 | 0, |
| 131 | 0, |
| 132 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 133 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 134 | |
| 135 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 136 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 137 | flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), |
| 138 | input0TensorShape.size()), |
| 139 | tensorType, |
| 140 | 0, |
| 141 | flatBufferBuilder.CreateString("input"), |
| 142 | quantizationParameters); |
| 143 | |
| 144 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 145 | flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), |
| 146 | input1TensorShape.size()), |
| 147 | ::tflite::TensorType_INT32, |
| 148 | 1, |
| 149 | flatBufferBuilder.CreateString("axis"), |
| 150 | quantizationParameters); |
| 151 | |
| 152 | // Create output tensor |
| 153 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 154 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 155 | outputTensorShape.size()), |
| 156 | tensorType, |
| 157 | 0, |
| 158 | flatBufferBuilder.CreateString("output"), |
| 159 | quantizationParameters); |
| 160 | |
| 161 | // create operator. Mean uses ReducerOptions. |
| 162 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; |
| 163 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); |
| 164 | |
| 165 | const std::vector<int> operatorInputs{ {0, 1} }; |
| 166 | const std::vector<int> operatorOutputs{ 2 }; |
| 167 | flatbuffers::Offset <Operator> controlOperator = |
| 168 | CreateOperator(flatBufferBuilder, |
| 169 | 0, |
| 170 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 171 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 172 | operatorBuiltinOptionsType, |
| 173 | operatorBuiltinOptions); |
| 174 | |
| 175 | const std::vector<int> subgraphInputs{ {0, 1} }; |
| 176 | const std::vector<int> subgraphOutputs{ 2 }; |
| 177 | flatbuffers::Offset <SubGraph> subgraph = |
| 178 | CreateSubGraph(flatBufferBuilder, |
| 179 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 180 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 181 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 182 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 183 | |
| 184 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 185 | flatBufferBuilder.CreateString("ArmnnDelegate: Mean Operator Model"); |
| 186 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); |
| 187 | |
| 188 | flatbuffers::Offset <Model> flatbufferModel = |
| 189 | CreateModel(flatBufferBuilder, |
| 190 | TFLITE_SCHEMA_VERSION, |
| 191 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 192 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 193 | modelDescription, |
| 194 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 195 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 196 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 197 | |
| 198 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 199 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 200 | } |
| 201 | |
| 202 | template <typename T> |
| 203 | void ConcatenationTest(tflite::BuiltinOperator controlOperatorCode, |
| 204 | tflite::TensorType tensorType, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 205 | std::vector<int32_t>& inputShapes, |
| 206 | std::vector<int32_t>& expectedOutputShape, |
| 207 | std::vector<std::vector<T>>& inputValues, |
| 208 | std::vector<T>& expectedOutputValues, |
| 209 | int32_t axis = 0, |
| 210 | float quantScale = 1.0f, |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 211 | int quantOffset = 0, |
| 212 | const std::vector<armnn::BackendId>& backends = {}) |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 213 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 214 | using namespace delegateTestInterpreter; |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 215 | std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode, |
| 216 | tensorType, |
| 217 | inputShapes, |
| 218 | expectedOutputShape, |
| 219 | inputValues.size(), |
| 220 | axis, |
| 221 | quantScale, |
| 222 | quantOffset); |
| 223 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 224 | // Setup interpreter with just TFLite Runtime. |
| 225 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 226 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 227 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 228 | // Setup interpreter with Arm NN Delegate applied. |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 229 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 230 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 231 | |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 232 | for (unsigned int i = 0; i < inputValues.size(); ++i) |
| 233 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 234 | CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues[i], i) == kTfLiteOk); |
| 235 | CHECK(armnnInterpreter.FillInputTensor<T>(inputValues[i], i) == kTfLiteOk); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 236 | } |
| 237 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 238 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| 239 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| 240 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 241 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 242 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| 243 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| 244 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 245 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 246 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| 247 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| 248 | |
| 249 | tfLiteInterpreter.Cleanup(); |
| 250 | armnnInterpreter.Cleanup(); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 251 | } |
| 252 | |
| 253 | template <typename T> |
| 254 | void MeanTest(tflite::BuiltinOperator controlOperatorCode, |
| 255 | tflite::TensorType tensorType, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 256 | std::vector<int32_t>& input0Shape, |
| 257 | std::vector<int32_t>& input1Shape, |
| 258 | std::vector<int32_t>& expectedOutputShape, |
| 259 | std::vector<T>& input0Values, |
| 260 | std::vector<int32_t>& input1Values, |
| 261 | std::vector<T>& expectedOutputValues, |
| 262 | const bool keepDims, |
| 263 | float quantScale = 1.0f, |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 264 | int quantOffset = 0, |
| 265 | const std::vector<armnn::BackendId>& backends = {}) |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 266 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 267 | using namespace delegateTestInterpreter; |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 268 | std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode, |
| 269 | tensorType, |
| 270 | input0Shape, |
| 271 | input1Shape, |
| 272 | expectedOutputShape, |
| 273 | input1Values, |
| 274 | keepDims, |
| 275 | quantScale, |
| 276 | quantOffset); |
| 277 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 278 | // Setup interpreter with just TFLite Runtime. |
| 279 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 280 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| 281 | CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk); |
| 282 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| 283 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| 284 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 285 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 286 | // Setup interpreter with Arm NN Delegate applied. |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 287 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 288 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| 289 | CHECK(armnnInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk); |
| 290 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| 291 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| 292 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 293 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 294 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| 295 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 296 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 297 | tfLiteInterpreter.Cleanup(); |
| 298 | armnnInterpreter.Cleanup(); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 299 | } |
| 300 | |
| 301 | } // anonymous namespace |